In literature, substantial work has been done on HSIC and there is a growing trend in the development of such techniques as shown in Figure REF . Most HSIC frameworks seemed to be influenced by the methodologies used in the computer vision domain [1]}. Traditional machine learning-based HSIC approaches use hand-crafted features to train the classifier. These methods generally rely on utilizing engineering skills and domain expertise to design several human-engineered features, for instance, shape, texture, color, shape, spectral and spatial details. All these features are basic characteristics of an image and carry effective information for image classification. Commonly used hand-crafted feature extraction and classification methods include: texture descriptors such as Local Binary Patterns (LBPs) [2]}, Histogram of Oriented Gradients (HOG) [3]}, Global Image Scale-invariant Transform / Global Invariant Scalable Transform (GIST) [4]}, Pyramid Histogram of Oriented Gradients (PHOG), Scale-invariant Feature Transform (SIFT) [5]}, Random Forests [6]}, kernel-based Support Vector Machine (SVM) [7]}, K-nearest Neighbours (KNN), and Extreme Learning Machine (ELM).
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